Fact tables generally consist of numeric values, and foreign keys to dimensional data where descriptive information is kept.Fact tables are designed to a low level of uniform detail (referred to as "granularity" or "grain"), meaning facts can record events at a very atomic level.
Dimensions can define a wide variety of characteristics, but some of the most common attributes defined by dimension tables include: Dimension tables are generally assigned a surrogate primary key, usually a single-column integer data type, mapped to the combination of dimension attributes that form the natural key.
Star schemas are denormalized, meaning the normal rules of normalization applied to transactional relational databases are relaxed during star schema design and implementation.
The benefits of star schema denormalization are: The main disadvantage of the star schema is that data integrity is not enforced as well as it is in a highly normalized database.
Related dimension attribute examples include product models, product colors, product sizes, geographic locations, and salesperson names.
A star schema that has many dimensions is sometimes called a centipede schema.
Having dimensions of only a few attributes, while simpler to maintain, results in queries with many table joins and makes the star schema less easy to use.Fact tables record measurements or metrics for a specific event.The star schema consists of one or more fact tables referencing any number of dimension tables.The star schema is an important special case of the snowflake schema, and is more effective for handling simpler queries.The star schema separates business process data into facts, which hold the measurable, quantitative data about a business, and dimensions which are descriptive attributes related to fact data.Examples of fact data include sales price, sale quantity, and time, distance, speed, and weight measurements.